Antenna Season Report Notebook¶

Josh Dillon, Last Revised January 2022

This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.

In [1]:
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
In [2]:
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
In [3]:
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "223"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
In [4]:
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))

Antenna 223 Report

In [5]:
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
In [6]:
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 30 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 30 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
In [7]:
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'

Load relevant info from summary CSVs¶

In [8]:
this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break
In [9]:
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])

Table 1: Per-Night RTP Summary Info For This Atenna¶

This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.

In [10]:
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))

Antenna 223, Node N19:

Out[10]:
JDs A Priori Status Auto Metrics Flags Dead Fraction in Ant Metrics (Jee) Dead Fraction in Ant Metrics (Jnn) Crossed Fraction in Ant Metrics Flag Fraction Before Redcal Flagged By Redcal chi^2 Fraction ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score Average Dead Ant Metric (Jee) Average Dead Ant Metric (Jnn) Average Crossed Ant Metric Median chi^2 Per Antenna (Jee) Median chi^2 Per Antenna (Jnn)
2460013 RF_ok 100.00% 0.00% 0.00% 0.00% - - -1.578311 -0.986495 -0.936412 -0.272840 -0.905144 -0.882657 0.952124 10.125756 0.5589 0.5844 0.3549 nan nan
2460012 RF_ok 100.00% 0.00% 0.00% 0.00% - - -1.420909 -0.876901 -1.090543 -0.421621 -0.846697 -0.809665 1.019251 12.780218 0.5641 0.5886 0.3485 nan nan
2460011 RF_ok 100.00% 0.00% 0.00% 0.00% - - -1.218971 -1.317730 -1.701449 -0.341322 -1.759015 24.722641 0.963285 15.738501 0.5872 0.5732 0.3385 nan nan
2460010 RF_ok 100.00% 0.00% 0.00% 0.00% - - -1.572680 3.213398 -1.457458 2.515851 -1.141909 0.979279 0.677109 5.453007 0.5999 0.5458 0.3582 nan nan
2460009 RF_ok 100.00% 0.00% 0.00% 0.00% - - -1.712691 -0.262375 -1.286970 1.753871 -1.268866 7.397205 -0.158620 4.033556 0.6040 0.5716 0.3527 nan nan
2460008 RF_ok 100.00% 0.00% 0.00% 0.00% - - -1.608065 -1.327396 -1.643242 -1.143361 -0.729114 29.926970 -0.875303 1.741663 0.6366 0.6396 0.3151 nan nan
2460007 RF_ok 0.00% 0.00% 0.00% 0.00% - - -0.787062 -0.664138 -1.114936 0.180858 -1.128616 -0.591890 0.877537 1.615155 0.5992 0.6307 0.3423 nan nan
2459999 RF_ok 0.00% 0.00% 0.00% 0.00% - - nan nan nan nan nan nan nan nan 0.5827 0.6323 0.3313 nan nan
2459998 RF_ok 0.00% 0.00% 0.00% 0.00% - - -0.988535 -0.213860 -1.026019 0.316529 -1.368942 -0.633423 1.157536 1.642301 0.5814 0.6206 0.3817 nan nan
2459997 RF_ok 100.00% 100.00% 99.95% 0.00% - - 232.089025 232.496085 inf inf 3285.790785 3246.874969 11439.124670 11086.449125 0.0266 0.2221 0.1667 nan nan
2459996 RF_ok 100.00% 0.00% 0.00% 0.00% - - -0.635734 -0.819661 -0.857803 -0.520105 -1.048651 35.969433 0.662169 9.593281 0.6025 0.6107 0.3839 nan nan
2459995 RF_ok 100.00% 0.00% 0.00% 0.00% - - -0.617347 -0.698694 -1.425219 0.143460 -0.752258 4.507177 0.511688 2.358657 0.5952 0.6328 0.3819 nan nan
2459994 RF_ok 0.00% 0.00% 0.00% 0.00% - - -0.670811 -0.359243 -1.268094 0.344936 -0.631572 -0.394147 0.849953 0.088593 0.5878 0.6272 0.3773 nan nan
2459993 RF_ok 100.00% 100.00% 100.00% 0.00% - - 274.356537 274.191540 inf inf 2635.247873 2613.935531 4162.904118 4027.409677 nan nan nan nan nan
2459991 RF_ok 100.00% 99.95% 99.95% 0.00% - - nan nan inf inf nan nan nan nan 0.3852 0.4429 0.3560 nan nan
2459990 RF_ok 100.00% 97.14% 96.92% 0.11% - - 234.405594 234.608638 inf inf 3224.873189 3097.697103 6041.874872 5562.902143 0.3346 0.3876 0.3092 nan nan
2459989 RF_ok 0.00% 0.00% 0.00% 0.00% - - -0.720883 -0.189113 -1.070481 0.509192 -0.560049 -0.785400 0.918677 -0.007409 0.5847 0.6254 0.3877 nan nan
2459988 RF_ok 0.00% 0.00% 0.00% 0.00% - - -0.700691 0.004294 -1.492852 0.655736 -0.597379 -0.315398 1.064399 0.064552 0.5851 0.6259 0.3776 nan nan
2459987 RF_ok 100.00% 93.03% 92.87% 0.05% - - 171.502172 169.374511 inf inf 2552.646447 2579.382600 4935.691719 5411.927519 0.3898 0.3944 0.3312 nan nan
2459986 RF_ok 0.00% 0.00% 0.00% 0.00% - - -0.398709 -0.153834 -1.448384 0.566557 -0.332904 -0.165545 0.204840 0.502997 0.6115 0.6514 0.3407 nan nan
2459985 RF_ok 0.00% 0.00% 0.00% 0.00% - - -0.689231 -0.097124 -1.310635 0.305124 -0.752035 -0.018191 1.356959 0.690585 0.5927 0.6304 0.3821 nan nan
2459984 RF_ok 100.00% 0.00% 0.00% 0.00% - - -0.960050 -0.506773 -1.251943 -0.036578 -1.306700 18.598146 -0.448319 0.542642 0.6123 0.6459 0.3607 nan nan
2459983 RF_ok 0.00% 0.00% 0.00% 0.00% - - -1.182316 -0.281893 -1.368164 0.527987 -1.038544 -0.086296 -0.054116 -0.018780 0.6199 0.6604 0.3317 nan nan
2459982 RF_ok 0.00% 0.00% 0.00% 0.00% - - -0.604318 -0.847286 -0.960201 -0.029233 -1.148974 -1.101498 -0.875881 -0.934788 0.6844 0.6927 0.2865 nan nan
2459981 RF_ok 0.00% 0.00% 0.00% 0.00% - - -1.076232 -0.314108 -1.579567 0.753803 -0.427473 -0.390942 0.628816 2.259203 0.5977 0.6331 0.3809 nan nan
2459980 RF_ok 0.00% 0.00% 0.00% 0.00% - - -1.151959 -0.774696 -1.534824 0.118107 -1.114431 -0.991187 -1.034550 -0.031612 0.6416 0.6659 0.3056 nan nan
2459979 RF_ok 0.00% 0.00% 0.00% 0.00% - - -1.058385 -0.569053 -1.534641 0.188024 -0.674079 -1.150934 1.045293 2.247288 0.5875 0.6278 0.3839 nan nan
2459978 RF_ok 0.00% 0.00% 0.00% 0.00% - - -1.050480 -0.438794 -1.555237 0.412312 -0.516237 -0.774471 1.384222 3.241894 0.5876 0.6264 0.3905 nan nan
2459977 RF_ok 0.00% 0.00% 0.00% 0.00% - - -1.030051 -0.399974 -1.510827 0.194657 -0.884136 -0.659010 0.460122 2.372375 0.5517 0.5892 0.3500 nan nan
2459976 RF_ok 0.00% 0.00% 0.00% 0.00% - - -1.051269 -0.204526 -1.588337 0.327771 -0.517094 -0.786665 0.405089 2.001899 0.5961 0.6336 0.3799 nan nan

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

In [11]:
htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.

In [12]:
for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

Antenna 223: 2460013

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
223 N19 RF_ok nn Temporal Discontinuties 10.125756 -1.578311 -0.986495 -0.936412 -0.272840 -0.905144 -0.882657 0.952124 10.125756

Antenna 223: 2460012

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
223 N19 RF_ok nn Temporal Discontinuties 12.780218 -1.420909 -0.876901 -1.090543 -0.421621 -0.846697 -0.809665 1.019251 12.780218

Antenna 223: 2460011

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
223 N19 RF_ok nn Temporal Variability 24.722641 -1.218971 -1.317730 -1.701449 -0.341322 -1.759015 24.722641 0.963285 15.738501

Antenna 223: 2460010

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
223 N19 RF_ok nn Temporal Discontinuties 5.453007 -1.572680 3.213398 -1.457458 2.515851 -1.141909 0.979279 0.677109 5.453007

Antenna 223: 2460009

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
223 N19 RF_ok nn Temporal Variability 7.397205 -1.712691 -0.262375 -1.286970 1.753871 -1.268866 7.397205 -0.158620 4.033556

Antenna 223: 2460008

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
223 N19 RF_ok nn Temporal Variability 29.926970 -1.327396 -1.608065 -1.143361 -1.643242 29.926970 -0.729114 1.741663 -0.875303

Antenna 223: 2460007

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
223 N19 RF_ok nn Temporal Discontinuties 1.615155 -0.787062 -0.664138 -1.114936 0.180858 -1.128616 -0.591890 0.877537 1.615155

Antenna 223: 2459999

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
223 N19 RF_ok nn Shape nan nan nan nan nan nan nan nan nan

Antenna 223: 2459998

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
223 N19 RF_ok nn Temporal Discontinuties 1.642301 -0.988535 -0.213860 -1.026019 0.316529 -1.368942 -0.633423 1.157536 1.642301

Antenna 223: 2459997

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
223 N19 RF_ok ee Power inf 232.089025 232.496085 inf inf 3285.790785 3246.874969 11439.124670 11086.449125

Antenna 223: 2459996

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
223 N19 RF_ok nn Temporal Variability 35.969433 -0.635734 -0.819661 -0.857803 -0.520105 -1.048651 35.969433 0.662169 9.593281

Antenna 223: 2459995

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
223 N19 RF_ok nn Temporal Variability 4.507177 -0.617347 -0.698694 -1.425219 0.143460 -0.752258 4.507177 0.511688 2.358657

Antenna 223: 2459994

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
223 N19 RF_ok ee Temporal Discontinuties 0.849953 -0.670811 -0.359243 -1.268094 0.344936 -0.631572 -0.394147 0.849953 0.088593

Antenna 223: 2459993

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
223 N19 RF_ok ee Power inf 274.356537 274.191540 inf inf 2635.247873 2613.935531 4162.904118 4027.409677

Antenna 223: 2459991

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
223 N19 RF_ok ee Shape nan nan nan inf inf nan nan nan nan

Antenna 223: 2459990

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
223 N19 RF_ok nn Power inf 234.608638 234.405594 inf inf 3097.697103 3224.873189 5562.902143 6041.874872

Antenna 223: 2459989

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
223 N19 RF_ok ee Temporal Discontinuties 0.918677 -0.189113 -0.720883 0.509192 -1.070481 -0.785400 -0.560049 -0.007409 0.918677

Antenna 223: 2459988

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
223 N19 RF_ok ee Temporal Discontinuties 1.064399 0.004294 -0.700691 0.655736 -1.492852 -0.315398 -0.597379 0.064552 1.064399

Antenna 223: 2459987

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
223 N19 RF_ok ee Power inf 171.502172 169.374511 inf inf 2552.646447 2579.382600 4935.691719 5411.927519

Antenna 223: 2459986

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
223 N19 RF_ok nn Power 0.566557 -0.153834 -0.398709 0.566557 -1.448384 -0.165545 -0.332904 0.502997 0.204840

Antenna 223: 2459985

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
223 N19 RF_ok ee Temporal Discontinuties 1.356959 -0.097124 -0.689231 0.305124 -1.310635 -0.018191 -0.752035 0.690585 1.356959

Antenna 223: 2459984

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
223 N19 RF_ok nn Temporal Variability 18.598146 -0.960050 -0.506773 -1.251943 -0.036578 -1.306700 18.598146 -0.448319 0.542642

Antenna 223: 2459983

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
223 N19 RF_ok nn Power 0.527987 -1.182316 -0.281893 -1.368164 0.527987 -1.038544 -0.086296 -0.054116 -0.018780

Antenna 223: 2459982

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
223 N19 RF_ok nn Power -0.029233 -0.604318 -0.847286 -0.960201 -0.029233 -1.148974 -1.101498 -0.875881 -0.934788

Antenna 223: 2459981

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
223 N19 RF_ok nn Temporal Discontinuties 2.259203 -0.314108 -1.076232 0.753803 -1.579567 -0.390942 -0.427473 2.259203 0.628816

Antenna 223: 2459980

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
223 N19 RF_ok nn Power 0.118107 -0.774696 -1.151959 0.118107 -1.534824 -0.991187 -1.114431 -0.031612 -1.034550

Antenna 223: 2459979

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
223 N19 RF_ok nn Temporal Discontinuties 2.247288 -1.058385 -0.569053 -1.534641 0.188024 -0.674079 -1.150934 1.045293 2.247288

Antenna 223: 2459978

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
223 N19 RF_ok nn Temporal Discontinuties 3.241894 -0.438794 -1.050480 0.412312 -1.555237 -0.774471 -0.516237 3.241894 1.384222

Antenna 223: 2459977

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
223 N19 RF_ok nn Temporal Discontinuties 2.372375 -1.030051 -0.399974 -1.510827 0.194657 -0.884136 -0.659010 0.460122 2.372375

Antenna 223: 2459976

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
223 N19 RF_ok nn Temporal Discontinuties 2.001899 -0.204526 -1.051269 0.327771 -1.588337 -0.786665 -0.517094 2.001899 0.405089
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